Computer vision inches toward ‘common sense’ with Facebook’s latest research - TechCrunch
Apr 30, 2021 1 min, 3 secs
Machine learning is capable of doing all sorts of things as long as you have the data to teach it how.

The company’s formidable AI research division has been working for years now on how to advance and scale things like advanced computer vision algorithms, and has made steady progress, generally shared with the rest of the research community.

Generally when you think of training an AI, you think of something like the aforementioned 500 pictures of cats — images that have been selected and labeled (which can mean outlining the cat, putting a box around the cat or just saying there’s a cat in there somewhere) so that the machine learning system can put together an algorithm to automate the process of cat recognition.

The DINO system (which stands rather unconvincingly for “DIstillation of knowledge with NO labels”) is capable of learning to find objects of interest in videos of people, animals and objects quite well without any labeled data whatsoever.

There’s also an adjacent research project, a training method called PAWS, which further reduces the need for labeled data

PAWS combines some of the ideas of semi-supervised learning with the more traditional supervised method, essentially giving the training a boost by letting it learn from both the labeled and unlabeled data


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